4 research outputs found
The Analysis of Alpha Beta Pruning and MTD(f) Algorithm to Determine the Best Algorithm to be Implemented at Connect Four Prototype
Connect Four is a two-player game which the players take turns dropping discs into a grid to connect 4 of one’s own discs next to each other vertically, horizontally, or diagonally. At Connect Four, Computer requires artificial intelligence (AI) in order to play properly like human. There are many AI algorithms that can be implemented to Connect Four, but the suitable algorithms are unknown. The suitable algorithm means optimal in choosing move and its execution time is not slow at search depth which is deep enough. In this research, analysis and comparison between standard alpha beta (AB) Pruning and MTD(f) will be carried out at the prototype of Connect Four in terms of optimality (win percentage) and speed (execution time and the number of leaf nodes). Experiments are carried out by running computer versus computer mode with 12 different conditions, i.e. varied search depth (5 through 10) and who moves first. The percentage achieved by MTD(f) based on experiments is win 45,83%, lose 37,5% and draw 16,67%. In the experiments with search depth 8, MTD(f) execution time is 35, 19% faster and evaluate 56,27% fewer leaf nodes than AB Pruning. The results of this research are MTD(f) is as optimal as AB Pruning at Connect Four prototype, but MTD(f) on average is faster and evaluates fewer leaf nodes than AB Pruning. The execution time of MTD(f) is not slow and much faster than AB Pruning at search depth which is deep enough
Influence map-based pathfinding algorithms in video games
Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems
by intelligent agents, ranging from computer games and applications to robotics. Pathfinding
is a particular kind of search, in which the objective is to find a path between two nodes. A
node is a point in space where an intelligent agent can travel. Moving agents in physical or
virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able
to navigate through its surrounding environment without avoiding obstacles, it does not seem
intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games.
Pathfinding algorithms work well with single agents navigating through an environment. In realtime
strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large
and dynamic game environments. On the contrary, influence maps are not used in pathfinding.
Influence maps are a spatial reasoning technique that helps bots and players to take decisions
about the course of the game. Influence map represent game information, e.g., events and
faction power distribution, and is ultimately used to provide game agents knowledge to take
strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g.,
capture an enemy location and win the game. Tactical decisions are based on small and precise
actions, e.g., where to install a turret, where to hide from the enemy.
This dissertation work focuses on a novel path search method, that combines the state-of-theart
pathfinding algorithms with influence maps in order to achieve better time performance and
less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho
mais curto, desde a à rea jogos de computador até à robótica. Pathfinding é um tipo
particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto
entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar.
Agentes móveis em mundos fÃsicos e virtuais são uma componente chave para a simulação de
comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia
sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz
parte das tarefas fundamentais de inteligencia artificial em vÃdeo jogos.
Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em
jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação
multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados
no pathfinding. Influence maps são uma técnica de raciocÃnio espacial que ajudam agentes
inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam
informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para
fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões
estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona
do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por
exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo.
Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding
com influence maps, afim de alcançar melhores performances a nÃvel de tempo de pesquisa e
consumo de memória, assim como obter caminhos visualmente mais suaves
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Bayesian opponent modeling in adversarial game environments.
This thesis investigates the use of Bayesian analysis upon an opponent¿s behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent¿s actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes¿ theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes¿ rule yields a notable improvement in the performance of an agent once an opponent¿s style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold¿em, where a betting round-based approach proves useful in determining and counteracting an opponent¿s play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent ¿style¿.Engineering and Physical Sciences Research Council (EPSRC
Reinforcement learning for qualitative group behaviours applied to non-player computer game characters
This thesis investigates how to train the increasingly large cast of characters in modern
commercial computer games.
Modern computer games can contain hundreds or sometimes thousands of non-player characters
that each should act coherently in complex dynamic worlds, and engage appropriately
with other non-player characters and human players. Too often, it is obvious that computer controlled
characters are brainless zombies portraying the same repetitive hand-coded behaviour.
Commercial computer games would seem a natural domain for reinforcement learning and, as
the trend for selling games based on better graphics is peaking with the saturation of game
shelves with excellent graphics, it seems that better artificial intelligence is the next big thing.
The main contribution of this thesis is a novel style of utility function, group utility functions,
for reinforcement learning that could provide automated behaviour specification for large
numbers of computer game characters. Group utility functions allow arbitrary functions of the
characters’ performance to represent relationships between characters and groups of characters.
These qualitative relationships are learned alongside the main quantitative goal of the
characters. Group utility functions can be considered a multi-agent extension of the existing
programming by reward method and, an extension of the team utility function to be more
generic by replacing the sum function with potentially any other function. Hierarchical group
utility functions, which are group utility functions arranged in a tree structure, allow character
group relationships to be learned. For illustration, the empirical work shown uses the negative
standard deviation function to create balanced (or equal performance) behaviours. This
balanced behaviour can be learned between characters, groups and also, between groups and
single characters.
Empirical experiments show that a balancing group utility function can be used to engender
an equal performance between characters, groups, and groups and single characters. It
is shown that it is possible to trade some amount of quantitatively measured performance for
some qualitative behaviour using group utility functions. Further experiments show how the
results degrade as expected when the number of characters and groups is increased. Further
experimentation shows that using function approximation to approximate the learners’ value
functions is one possible way to overcome the issues of scale. All the experiments are undertaken
in a commercially available computer game engine.
In summary, this thesis contributes a novel type of utility function potentially suitable for
training many computer game characters and, empirical work on reinforcement learning used
in a modern computer game engine